Method an apparatus for obtaining reflow oven settings for soldering a PCB

ABSTRACT

An artificial neural network is trained to recognize inputted thermal and physical features of a printed circuit board, for providing settings for a reflow oven for obtaining acceptable soldering of the printed circuit board.

TECHNICAL FIELD OF THE INVENTION

This is a divisional of application Ser. No. 08/040,809 filed Mar. 31,1993, now U.S. Pat. No. 5,439,160.

The field of the present invention relates generally to flow solderingof printed circuit boards (PCB), and more particularly relates to thedetermination of settings of infrared (IR) reflow oven temperatureprofiles for obtaining acceptable soldering of given configurations ofPCB's.

BACKGROUND OF THE INVENTION

Modern electronic circuits require the use of printed circuit boards formounting and interconnecting electronic devices thereon. Typically, thevarious components mounted on a printed circuit board (PCB) are solderedto the board. One typical technique to solder components to a PCBconsists of placing the PCB on a conveyor, and moving the PCB across astanding wave of molten solder. In recent years, for obtaining moreprecise control of the soldering process, infrared (IR) reflow ovenshave been developed for providing a plurality of temperature zonesthrough which a PCB passes, for causing the reflow or remelting ofsolder placed on the PCB prior to running it through the oven, forsoldering components to the PCB. These ovens provide for closelycontrolled preheating of the PCB's, followed by sufficient heating in areflow zone to cause solder on the board to reflow for solderingcomponents, followed by a cycle of natural cool-down.

To accomplish the precise control required, for obtaining high qualitysoldering of components to a PCB, without delaminating the PCB material,or damaging electronic components on the board through overheating orheat stress cycling, it is important to adjust the temperature profileof the reflow oven to match the temperature profile required for theparticular PCB to be soldered. To accomplish this, a large number ofprinted circuit boards of the same type are passed through the ovenunder different heating conditions, to determine the best setting of theoven for obtaining optimum soldering of the PCB. Each time a differentPCB, or differently configured PCB is to be soldered, a relatively largenumber of identically configured PCB's must be run through the oven toobtain the optimum oven settings for soldering that configuration ofPCB. Thereafter, a production run is made for soldering a large numberof the identical PCB's.

During the process of obtaining the best oven settings for a particularPCB configuration, a significant number of test boards are typicallyscrapped. To overcome this problem, and to eliminate the time expendedin test soldering PCB's, various techniques have been developed in theprior art to reduce the number of PCB's required for obtaining the besttemperature profile for the setting of an oven to typically reflowsolder the PCB configuration. For example, devices sometimes known as"moles", have been developed for mounting upon a PCB, to monitor variousthermocouples strategically placed on the PCB, for remotely transmittingthe temperature data back to a remotely located receiver, for obtainingthe temperature profile of a PCB as it is passed through a reflow oven.One such temperature profile measuring system is for a flow soldersystem as shown and described in O'Rourke et al., U.S. Pat. No.4,180,199.

Also, an effort has been made in the prior art to develop systems forautomatically setting the controls for a reflow oven, for example, forcontrolling the temperature of various zones in the oven and theconveyor speed through the oven, in reflow soldering a PCB. One exampleof such a system is taught in Matsuo et al., U.S. Pat. No. 5,003,160.This system includes a data table for storing sets of control datarelating to conveyor speed, and operating temperatures of individualheaters in specific zones of the associated oven. A microprocessor isprogrammed to receive conditional parameters associated with an objectto be soldered, and respond by obtaining from the data table a set ofcontrol data closely associated with the combination of conditionalparameters of the object. Features of the PCB, such as thicknessthereof, surface area thereof, surface area of the largest component,type of material used, and the melting point of solder, are provided asinput data for a PCB to be soldered, to permit the microprocessor toobtain control data from the data table for setting up the reflow oven.As indicated in column 4, lines 27 through 35 of U.S. Pat. No.5,003,160, data stored in the data table is correlated between controldata and conditional parameters associated with a plurality of objectsto be soldered, respectively, for permitting the system to recognize thecombination of conditional parameters associated with a given object toobtain the appropriate control data from the data table. This data isused to set up the associated reflow oven.

The present inventor recognized that there is a need in the field of thepresent technology for obtaining more rapid identification of controlparameters for setting up an oven, such as a reflow oven, for optimizingthe reflow soldering of various PCB's, without requiring the use of avery large memory for establishing a data table storing a combination ofcontrol parameters for each one of a relatively large population ofdifferent PCB boards. He also recognized the need to optimize the ovensettings obtained for a specific PCB configuration, rather than usesettings previously established for a PCB that has the closest match tothe PCB to be soldered.

SUMMARY OF THE INVENTION

With the problems of the prior art in mind, it is an object of theinvention to provide an improved method and apparatus for the control ofIR reflow ovens in soldering a plurality of different PCBconfigurations.

Another object of the invention is to provide a method and apparatus fortraining an artificial neural network to respond to input featuresassociated with a particular PCB configuration, for automaticallydetermining the IR oven profile for controlling an IR oven to reflowsolder a new PCB.

These and other objects of the invention are carried out by programminga microprocessor with an artificial neural network, or by using adedicated neural network incorporated in an integrated circuit chip, andthereafter training the network using known zone temperatures fromexisting IR reflow oven profiles for test printed circuit boardspreviously run through the IR oven. Once the neural network is trained,it is used for obtaining the IR oven profiles for a new PCB. The new PCBis run through a test oven to obtain required reference temperatures.The reference temperatures together with a number of physical parametersprovide identifying features for inputting to the trained neural networkin a recall mode, for obtaining as outputs from the neural network theIR oven settings for obtaining high quality reflow soldering of the newboard.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the method and apparatus of the present inventionare described below with reference to the following drawings, in whichlike items are identified by the same reference designation, wherein:

FIG. 1 is a simplified block diagram of the apparatus for one embodimentof the present invention.

FIG. 2 is a simplified interior view of an example of a reflow ovenassociated with various embodiments of the invention.

FIG. 3 shows a typical IR reflow solder profile for soldering a printedcircuit board in an IR reflow oven controlled to provide the temperatureprofile shown.

FIG. 4 shows the location of thermocouples on a PCB typically used toobtain the temperature profile for a PCB as it is passed through an IRreflow oven, in this example.

FIG. 5 shows typical temperature profiles obtained using a mole typetemperature logger in association with various embodiments of thepresent invention.

FIG. 6 shows an IR test oven setup for obtaining reference temperaturesfor a new PCB, for application as input features to train an artificialneural network in one embodiment of the invention.

FIG. 7 shows a simplified pictorial diagram of an artificial neuralnetwork of one embodiment of the invention.

FIG. 8 shows a listing of features for application to an artificialneural network in one embodiment of the invention.

FIG. 9 shows a feature listing of actual values for a typical PCBrelative to the features as listed in FIG. 8.

DETAILED DESCRIPTION

In general terms, the present method and apparatus of the invention donot require a model of a PCB to be soldered for obtaining the IR ovenzone settings for setting up the associated oven for soldering theboard. The present invention provides for consideration of variousphysical and thermal properties of the PCB, as will be shown below, forpermitting one to quickly and easily obtain the required IR oven zonesettings for optimally soldering the PCB of interest.

More specifically, with reference to FIGS. 1 and 2, for a representativesample of boards 18, it is required that the IR oven 1 temperatureprofiles for these boards 18 be obtained. This is accomplished byobtaining a sufficient number of each one of sample board 18configurations, and running them through the associated IR reflow oven 1under different conditions, such as using different combinations of zonetemperatures and conveyor belt 14 speeds, for selecting the best IR oven1 temperature profile for reflow soldering that particular board 18configuration,.

The next step is to configure the associated IR oven 1 with apredetermined set of zones Zn01-Zn10 (see FIG. 2), establishtemperatures for selected zones (in this example zones Zn01-Zn08, seeFIG. 6), and the speed of conveyor belt 14. Next, each one of the sampleboards 18 are individually equipped with a temperature measurementdevice 20, and run through the oven 1, for obtaining input featuresassociated with each individual PCB 18, respectively. These temperatureparameters are obtained by noting the temperature of the sensors orthermocouples 24 (see FIG. 4) on the PC board 18 being run through theoven 1 at several intervals during conveyance thereof through theoven 1. Identical measurement intervals are used for each of therepresentative samples of PCB's 18. As will be described in greaterdetail below, these reference temperatures are used along with physicaldata associated with the respective PCB's 18, such as the dimensionsthereof, number of integrated circuits (IC's), and so forth, to provideinput features for training an artificial neural network 7. In thisexample, the neural network is programmed into a microprocessor 5, suchas a personal computer.

After the artificial neural network 7 is trained, it is relatively easyto thereafter determine the IR oven profile for a new printed circuitboard 18, by first running the PCB 18 through the IR reflow oven setupwith the predetermined test zone temperatures and belt speed, forobtaining from the test oven configuration the reference temperaturesassociated witch the new PCB 18. These reference temperatures togetherwith the physical parameters of the new PCB 18, relative to therepresentative sample of printed circuit boards 18 used to train theneural network 7, are applied to the latter in the recall mode, forobtaining the IR reflow oven 1 zone settings for reflow soldering thenew PCB configuration. A more detailed description of the invention nowfollows below.

Apparatus for carrying out the method of the present invention is shownin FIG. 1, as an example not meant to be limiting. More specifically,this apparatus includes an IR reflow oven 1, a printer 2, an ovencontroller 3, a display 4, a microprocessor 5, an artificial neuralnetwork simulator 7, the latter being loaded into microprocessor 5, andan identified features list 9 for providing inputs to the artificialneural network 7 via microprocessor 5. In this example, themicroprocessor 5 is an IBM PC/AT compatible computer, including a DELL®325 personal computer, an AT386 central processing unit, and an 80387math co-processor. Also, the neural network 7 simulation package used inthis example is sold by NeuralWare, Inc., as "Neural Computing",NeuralWorks Professional II/PLUS, Version 4.01, released on Jan. 31,1991, for use in IBM® PC personal computers. A manual is included withthe "Neural Computing" package, and the teachings thereof areincorporated herein by reference to the extent they do not conflict withteachings hereof. However, the particular microprocessor 5 and neuralnetwork 7 for use with the present invention are not meant to be limitedto the aforesaid, and other microprocessors 5 and neural networksimulation packages 7 may be used in place of the previously mentionedcomputer and neural network simulation package 7 or dedicated neuralnetwork chip 7.

In this example, as shown in FIG. 2, an IR reflow oven 1 is configuredwith ten temperature zones Zn01 through Zn10, respectively. Each zoneincludes one or more infrared heaters 12, in this example. Also in thisexample, included in the oven 1 is a conveyor belt 14 driven by drivewheels 16, for moving a printed circuit board (PCB) 18 through thevarious zones Zn01-Zn10 of the oven at a predetermined speed, travellingfrom zone Zn01 through to zone Zn10, in this example. Also in thisillustration, the PCB 18 is shown to be carrying a mole temperaturemeasuring and logging device 20. The mole temperature measuring device20, in this example, is provided by a "M.O.L.E. Profiler, Multi-ChannelOccurrent Logger Evaluator", manufactured by Electronic Controls Design,Mulino, Oreg. 97042. The teachings of the "M.O.L.E. Operations Manual",dated Nov. 4, 1987, published by Electronic Controls Design, consistingof pages 1 through 25, is incorporated herein by reference, to theextent they do not conflict with the teachings hereof. However, othermole type loggers may be used in the present invention.

A typical cycle of operation for reflow soldering a printed circuitboard (PCB) 18 is shown in FIG. 3. The PCB 18 travels along conveyor 14in the direction 22, as shown in FIG. 2. Typically the speed of theconveyor 14 is held constant at some predetermined speed. The PCB 18first enters zone Zn01, and progresses sequentially through Successivezones, in this example, zones Zn02 through Zn10, respectively. Note thatthe zones Zn01 through Zn10 can be combined with adjacent zones forextending a particular heating of the board, through control of theheating elements 12. A typical infrared (IR) reflow solder profile ortemperature profile is shown in FIG. 3. As shown, for about the firstone-half minute of travel the PCB 18 is at room temperature, and for thenext one-half minute of travel it is rapidly preheated to 100° C. foractivating solder flux previously applied to the PCB 18 in areas to bereflow soldered. For the next approximately one-half minute of travel,PCB 18 continues to be preheated at 100° C. At time 1.5 minutes oftravel, for the next one-half minute, PCB 18 is further heated in a ramptype manner to about 150° C., in a manner insuring uniform heatingthereof. PCB 18 continues to be heated to increasingly highertemperatures in a linear-like manner, for obtaining at 2.5 minutes oftravel a temperature of about 183° C., for causing the solder to beginmelting, in this example. Between about 2.5 minutes and 3.0 minutes oftravel, the temperature is linearly increased to about 215° C. to 219°C., for insuring complete reflow soldering of PCB 18. After 3.0 minutesof travel, the temperature is decreased linearly, as shown, forpermitting PCB 18 to cool down in a natural manner toward roomtemperature. As shown, between about 2.5 minutes and 3.5 minutes oftravel, the temperature of the board is held above the solder meltingpoint of 183° C., in this example, for about 45 to 60 seconds. The IRreflow solder profile shown in FIG. 3 is for purposes of illustrationonly and many other IR reflow solder profiles may be applicable,depending upon the PCB 18 configuration and physical characteristics,the melting point of the particular solder used, the nature of thesolder fluxes used, and so forth.

As is known in the art, different PCB 18 configurations have differenttemperature profiles, which can be considered a fingerprint oridentification for the particular PCB 18 configuration, permitting it tobe distinguished from other different PCB 18 configurations. In thisexample, the mole 20 is mounted upon a PCB 18, and the PCB 18 is runthrough oven 1, as shown in FIG. 2, for obtaining the temperatureprofile of the particular PCB 18. Temperature transducers are mounted onthe PCB 18 at positions A, B, C, D, and E, respectively, as shown inFIG. 4, for example. The thermocouples or temperature sensors 24 areelectrically connected to mole 20, for permitting mole 20 to recordtemperature profile information as PCB 18 travels through oven 1 onconveyor 14. As shown in FIG. 5, typical temperature profile curves 26,28, 30, 32, and 34 may be logged by mole 20 for locations A, B, C, D,and E, respectively, on PCB 18, in this example. As will be shown ingreater detail below, these individual temperature profile curves areused to provide data which in combination with other physicalcharacteristics of the PCB 18 provide a fingerprint or identificationfor each different PCB 18 configuration. It is important that locationsA through E of thermocouples 24 that were used for obtaining theoriginal temperature profile data be the same as the placement used forobtaining the test oven 1 reference data for each specific PCB 18configuration. FIG. 4 shows typical locations on a PCB 18 ofthermocouples 24. It is also important that the test oven 1, in thisexample, be setup identically for each temperature profile measurementof a PCB 18 configuration, for identification purposes. In this example,oven 1 was set up as shown in FIG. 6. As shown, conveyor 14 ismaintained at a speed of 75 centimeters per minute, and zones Zn01through Zn08 have their respective heaters 12 energized at the wattagelevels shown. In this example, heater 12 of zones Zn09 and Zn10 are notused. The suggested test oven 1 setup is not meant to be limiting, andmany other test oven 1 setups may be used for purposes of obtainingtemperature profile information for distinguishing between different PCB18 configurations.

Note that in FIGS. 3 and 5, as indicated, the abscissa of thetemperature profile curve shown has a time scale in minutes, in thisexample. Accordingly, from FIG. 3, it is assumed that the PCB 18 isconveyed via conveyor 14 through zones Zn01 through Zn10 in about 4.0minutes, and in FIG. 5 in about 5.0 minutes. Depending upon theapplication, many other conveyor 14 speeds may be used, and thetemperatures set differently for the various zones, which would resultin substantially different temperature profiles.

The present inventor recognized that for optimizing the performance ofneural network 7 in processing features of a particular PCB 18 forobtaining appropriate settings for oven 1, for reflow soldering the PCB18, it is important to establish a sufficient number of features andcarefully choose such features, for insuring high quality reflowsoldering of the associated PCB 18.

In FIG. 7, a simplified diagram of a neural network 7 is shown toinclude a layer comprising a plurality of input nodes 36, a second layerof nodes providing a plurality of output nodes 38, and a hidden layer ofa plurality of nodes 40. As previously indicated, neural network 7 isprogrammed into microprocessor 5, in this example. For purposes oftraining neural network 7, the present inventor discovered aftersubstantial experimentation that the IR reflow oven feature list entriesshown in FIG. 8 provided optimal training of neural network 7. Suchtraining thereafter permits neural network 7 to accept input features ofa new PCB 18, and produce output settings for oven 1 for reflowsoldering the new PCB 18. As shown in this example, the input featuresare identified as In#1 through In#23, and associated output featuresOUT#1 through OUT#11, respectively. Further reference is made to FIG. 8,in which the listing shown identifies and gives an explanation of eachone of these features. In FIG. 9, the actual values for a representativeone of a sample or representative group of different PCB boards 18 of arelated family is shown. Note that the sample of values shown are forpurposes of illustration only, and are not meant to be limiting.

Operation of the invention will now be described. First, one mustassemble samples of various PCB boards 18 that are representative of afamily of PCB boards 18 expected to be reflow soldered through oven 1.Next, for purposes of identifying each type of board as to inputfeatures, in this example the oven 1 is set up as shown in the chart ofFIG. 6, as previously explained. With oven 1 set up as indicated, a mole20 is mounted upon each one of the PCB boards 18, along with associatedtemperature sensors 24, as previously described, respectively, and eachis run through oven 1 for obtaining the IR oven temperature profile foreach representative PCB 18. The temperature profile is used to calculateinput features In#16 through In#23. Note that for input features In#16through In#19, as shown in FIG. 8, "x" is indicative of the travel timein seconds of the PCB 18 on conveyor 14 in traveling through oven 1. Theoutput features Out#1 through Out#11, are obtained by running aplurality of each one of the representative sample of PCB 18 throughoven 1, for a different combination of settings of the wattage toheaters 12 in zones Zn#01 through Zn#10, respectively, at apredetermined belt speed, for determining which combination provides theoptimal reflow soldering for the particular sample PCB 18 boardconfiguration, as previously described. Note also that the mole 20 wasused on a particular PCB 18 board of the representative sample set ofPCB boards 18 for making about ten runs through oven 1 for eachparticular configuration of PCB 18. From these ten temperature runs, thepreferred temperature profile for each particular PCB 18 board wasobtained.

After establishing the features for each one of the PC board 18 samplesof the representative set, the features list 9 (see FIGS. 1 and 8) isused for training neural network 7. In this example, training of neuralnetwork 7 is carried out using a backpropagation neural network 7. Foreach representative PCB 18, neural network 7 is trained by applying theinput features of FIG. 8 into microprocessor 5, for in turn applying theinput features In#1 through In#23 to individual input nodes 36,respectively, and associated output settings Out#1 through Out#10 todesignated output nodes 38. The latter are the desired outputsassociated with the designated inputs, for optimally setting the oven 1to reflow solder the particular PCB 18 associated therewith. The neuralnetwork simulation program 7, in the learn mode, generates outputs fromthe given inputs, compares these outputs with the desired outputssupplied to the output nodes, for developing error signals. The networkthen uses the error signals to adjust various weighting factors withinthe program for neural network 7 for changing the actual outputs to beas close as possible to the desired outputs Out#1 through Out#11,respectively. In other words, the programming for neural network 7 issuch to reduce the error signals to a predetermined minimum value. Thefeatures list 9 for each one of the sample PCB boards 18 of therepresentative set of boards is applied to neural network 7 for trainingthe network 7. During the training process, the features list 9 for thevarious boards are applied randomly to neural network 7 to insure thatthe network 7 does not learn parameters for only one PC board 18, oronly for a few representative PCB boards 18. Training of neural network7 is enhanced as the number of different samples is increased, therebyproviding a more accurate representation of the universe or family ofPCB boards 18. Also, it is important to terminate training when theerror between actual outputs and desired outputs reaches a predeterminedminimum value.

Note that for the features shown in FIG. 8 for a given sample PCB 18,the actual values must be normalized to a standard range of typically0-1, or -1 to +1, before being processed by the neural network. Thepresent neural network 7 as identified above using the software ofNeuralWare, Inc., automatically provides for such normalizing.

Various methods are used for determining the number of training examplesfor adequately training a neural network. For the type of neural network7 used in this example, the number of training examples is partly basedon the number of weights in the neural network 7. The weights are theunits or portions of the network trained to learn the input to output"mapping", and as such provide an estimate of the number of requiredtraining examples, and number of representative sample PCB boards 18,required in this example. For the present example, from the IR reflowoven 1 feature list 9 entries of FIG. 8, the number of training samplesrequired can be calculated, as discussed in an article by Casimir C."Casey" Kllmasauskas, entitled "Applying Neural Networks, Part III:Training a Neural Network", PC AI, May/June 1991, pages 20-24. Theteachings of this article are incorporated herein by reference, to theextent they do not conflict with the teachings hereof. Morespecifically, from the feature list entries of FIG. 8, following theexample of the article, one would obtain 23 input node weights plus onebias weight; 15 hidden unit weights, plus one bias weight; and 11 outputnode weights. From this, one would then perform the calculation of[(23+1)*15+(15+1)*11]=536. Applying the suggested multiple of 5guideline, one would require 2,680 sample PCB boards 18 for trainingneural network 7. Depending upon the application, one may be able toreduce the number of sample PCB boards 18 required, yet still be able tohave an acceptable set of output values when the trained neural network7 is used in recall mode on a new PCB 18. Once neural network 7 istrained, it must be thoroughly tested by using sample PCB boards 18 notused to train the network 7, to insure that the outputs being generatedon recall for new examples are within acceptable limits of error for theparticular application.

The features shown in FIG. 8 were determined by the present inventor foroptimizing the invention. However, in certain applications, the numberof features required may be less than those shown in FIG. 8. For anyparticular application, one must run tests or experiment in order todetermine whether the features can be reduced.

In this example, neural network 7, as provided by the "Neural Computing"software indicated above, was trained using the most simplifiedprocessing associated therewith. As indicated in the manual for thesoftware used, other much more complicated training techniques than thestraight Delta rule used in this example, can be considered for use intraining network 7.

During the training of neural network 7, printer 2 and display 4 may beused in order to follow the training session. Also, after the neuralnetwork 7 is trained, for a new PCB 18, for which settings for reflowsoldering thereof are obtained by applying the features list 9 for newPCB 18 to neural network 7, via microprocessor 5, as previouslydescribed. The output parameters from neural network 7 for the settingof oven 1 may be printed out on printer 2, and/or displayed on display4, and also provided to a oven controller 3. Oven controller 3 will thenrespond by setting up oven 1 for optimum reflow soldering of the new PCB18. In this manner, automated control of the oven 1 is obtained.However, in certain applications, the oven controller 3 may not be used,and the oven 1 would be manually set up by using the output settingsobtained from printer 2, or displayed on display 4.

Although various embodiments of the invention have been shown anddescribed herein, they are not meant to be limiting. Those of skill inthe art may recognize various modifications to one or more of thedescribed embodiments, which modifications are meant to be covered bythe spirit and scope of the appended claims. For example, the presentinvention may be applicable for broader use than just IR reflowsoldering, such as use with wave soldering equipment, vapor phasesoldering equipment, and so forth.

What is claimed is:
 1. An artificial intelligence system for processingfeature data associated with individual printed circuit boards (PCB)within a family of printed circuit boards having similar but notidentical combinations of characteristics, for producing as output datasettings for a reflow oven for soldering one of said PCB's, said systemcomprising:a neural network; and means for training said neural networkto receive identifying features of each individual PCB of said family ofPCB's, and produce as outputs belt speed and temperature zone settingsfor said reflow oven, for obtaining acceptable soldering for a selectedindividual PCB.
 2. The system of claim 1, wherein said means fortraining said neural network, includes:means for establishingpredetermined temperatures for each one of a plurality of temperaturezones in said reflow oven; means for individually conveying each one ofa representative set of differently configured sample PCB's from saidfamily of PCB's through said reflow oven at a predeterminedsubstantially constant speed; and temperature measuring means forobtaining thermal characteristics for each one of said sample PCB's,which thermal characteristics are selected and used in combination withidentifiable physical characteristics for each said sample PCB, therebyproviding identifying and distinguishing input features between eachsaid sample PCB, whereby these input features are applied to input nodesof said neural network, in combination with output features ofpredetermined oven settings and conveyor belt speed for obtainingacceptable soldering of the associated said sample PCB applied to outputnodes of said neural network, for training said neural network, saidinput and output features for each one of said sample PCB's beingapplied in a random manner to said neural network.
 3. A system forautomatically determining settings for a reflow oven for soldering aprinted circuit board (PCB) comprising:a microprocessor; programmingmeans for installing a neural network program into said microprocessor;a reflow oven including a plurality of temperature zones, and a motordriven conveyor belt for moving a PCB through said plurality oftemperature zones; means for determining thermal characteristics for arepresentative plurality of different samples of printed circuit boardstypical of a family of printed circuit boards that may be run throughsaid reflow oven, whereby for each sample PCB, certain thermalcharacteristics are selected and used in combination with identifiablephysical characteristics for each sample PCB, for obtaining identifyingand distinguishing input features between each sample PCB; means fordetermining for each of said plurality of sample PCB boards outputfeatures including the belt speed, and temperature settings for saidtemperature zones, respectively, of said oven for obtaining acceptablesoldering of said sample PCB boards; and means for training said neuralnetwork by sequentially and iteratively applying via said microprocessorsaid input features to input nodes of said neural network, and saidoutput features to output nodes of said neural network, respectively foreach sample PCB, whereafter said neural network is operable in a recallmode for receiving features of a non-sample PCB, for providing asoutputs from said neural network a desired belt speed, and temperaturesettings for each zone of said oven, for setting up said oven to soldersaid non-sample PCB.
 4. The system of claim 3 further including:outputmeans driven by said microprocessor for providing a visual indication ofdata applied to said neural network, and resulting output data from saidneural network.
 5. The system of claim 3, further including:ovencontroller means for receiving output data of said neural network fromsaid microprocessor, for automatically setting up said reflow oven forsoldering said non-sample PCB.
 6. A controllable oven for soldering aprinted circuit board, comprising:artificial neural network meanscoupled to said oven and being trained to recognize inputted thermal andphysical characteristics of a printed circuit board; and means forcausing said artificial neural network means to provide settingsincluding those for a plurality of temperature zones for said oven forobtaining acceptable soldering of said printed circuit board.